Udacity’s Self-Driving Car Nanodegree

We are building a Self-Driving Car Nanodegree at Udacity!

Please sign up here if you are interested in learning more. Limited enrollment will begin later this summer.

The Self-Driving Car Nanodegree involves courses and projects that will give you hands-on experience with deep learning, robotics, computer vision, sensors, and hardware. It will be a blast.

One of the best parts is that Sebastian Thrun will be leading the nanodegree with me!

Sebastian is the co-founder of Udacity, and also a rockstar in the world of autonomous vehicles. Before Udacity, Sebastian launched Google’s Self-Driving Car program. And even before that, he won the DARPA Grand Challenge self-driving car race as a Stanford professor.

I have had so much fun learning about self-driving cars with Sebastian, and I’m confident you will, too.

My own career in self-driving cars started with Udacity’s Artificial Intelligence for Robotics course, and I followed that with an arduous combination of online courses and projects and networking.

Our goal with the Self-Driving Car Nanodegree is to create a program that is fun and comprehensive and not at all arduous.

Most of all, we want to help you learn the skills and techniques from the most advanced autonomous vehicle companies in the world!

So, please, sign up here to learn more, and join us as part of the first class of self-driving car engineers at Udacity!

Starting at Udacity

New week, new job 🙂

A few weeks ago I got an offer to join the online education startup Udacity.

The offer was amazing, not so much because of the compensation (although that is great, thanks), but because of the opportunity to work on an amazing project.

I have a lot of experience participating in Udacity courses as a student. Their Artificial Intelligence for Robotics course was the very first step I took in becoming an autonomous vehicle engineer. I’ve also taken Udacity courses in parallel programming, deep learning, and other areas.

So I accepted the offer to join Udacity. I am crazy excited to start today.

I love Ford, I loved working at Ford, I recommend Ford highly to anyone hoping to work on autonomous vehicles, and I even hope to return there eventually.

But I think that what we’re building at Udacity justifies the decision.

The project is under wraps for a little while longer, but I don’t think I’m giving away the store to say that I will continue to work on self-driving cars. In fact, I’ll be working with Udacity co-founder Sebastian Thrun, who also won the DARPA Grand Challenge and founded Google’s self-driving car project.

Stay tuned!

Driving Might Become the New Biking

Recently I’ve had a few discussions with people who are nervous about self-driving cars, mostly because they find driving fun. They’re worried that we’re entering a brave new world where people won’t be allowed to drive for fun anymore.

I think this is a legitimate concern, but my response is that driving will become like biking.

Biking today is primarily a leisure activity that people do for fun. Except in a certain uncommon (and usually urban) instances, biking is rarely the most efficient or fastest way to transport yourself.

Once self-driving cars become common, I expect to see much the same thing. We might see certain roads designated only for human-driven cars, just like many paths are designated specifically for bikes today.

And it won’t shock me if we see a replay of some of the cyclist vs. driver road rage in the form of human drivers vs human passengers in self-driving cars, all trying to use the same road.

My model for thinking about how human-driven cars will map onto the self-driving road system is to think about how bikes map onto the current human-driven road system.

There are a lot of bike-only paths, often along scenic routes. Outside of those routes, cyclists will often use slower, smaller, residential streets for biking. The instances in which cyclists need to use main commuting thoroughfares are the situations in which bike-car conflict is the greatest.

So I can imagine scenic roads, like US 1 in California, or Skyline Drive in Virginia, being set aside specifically for human drivers. This might be especially true if self-driving cars ultimately attain speeds far beyond what human drivers can safely handle today.

Big interstate highways, though, might become the domain of computer-driven cars traveling hundreds of miles per hour.

CS373

I just started CS373: Artifical Intelligence for Robotics, which is Sebastian Thrun‘s robot car course on Udacity.

Thrun is an Elon Musk-type, who has been wildly successful in a number of disparate domains — Stanford professor, father of the self-driving car, Udacity CEO. There’s a lot to say about Thrun on another occasion, but here I’ll focus on the Udacity robotics course.

This is the first course I have taken on the Udacity platform, and I am really impressed by what they have put together. The format is a big advance over the lectures I listened to in college.

For one, Thurn has moved way beyond putting his PowerPoint slides into a YouTube video and doing a voiceover. Instead, Thrun is basically doing a very polished whiteboard presentation, specially crafted for the Udacity format. Which means we’re not looking at Thurn standing at a whiteboard, but rather we’re looking at his hand (or that of a hand model), drawing out well-contained lessons.

But the big step forward is the constant quiz and feedback mode. Every 1–2 minutes, Thrun will ask a quiz questions, to verify we’re still following along. Sometimes it’s a multiple-choice question; often it’s a toy programming problem which requires we write 2–5 lines of Python in the context of a larger program that he gives us.

Thrun is very enthusiastic, constantly telling us how amazing and remarkable we are as students, to have so quickly programmed up a toy version of the Google self-driving car localization algorithm.

In reality, I think it is Thrun who has built something quite remarkable.

Originally published at www.davidincalifornia.com on October 5, 2015.